Notebook for generating mutual rank (MR) clusters
library(boxr)
Welcome to boxr 0.3.4.99999!
Bug reports & Getting Help: https://github.com/brendan-R/boxr/issues
library(magrittr)
library(knitr)
library(limma)
library(snowfall)
Loading required package: snow
library(igraph)
Attaching package: ‘igraph’
The following object is masked from ‘package:magrittr’:
%>%
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
box_auth()
Reading client id from .Renviron
Reading client secret from .Renviron
Auto-refreshing stale OAuth token.
boxr: Authenticated at box.com as Julin Maloof (jnmaloof@ucdavis.edu)
box_setwd(24503987860)
box.com working directory changed to 'BrapaNetworks'
id: 24503987860
tree: All Files/BrapaNetworks
owner: jnmaloof@ucdavis.edu
contents: 9 files, 0 folders
box_ls()
box.com remote object list (9 objects)
Summary of first 9:
name type id size
1 Brapa2011BayesHeight_blups.csv file 164446516413 15 kB
2 Brapa2012BayesHeight_blups.csv file 164446639332 28 kB
3 Brapa_V1.5_annotated.csv.gz file 163054185089 2.7 MB
4 brassica_gene_clusters.RData file 164456845216 110 kB
5 CountsVoom.Rdata file 162362592785 240 MB
6 NormalizedCounts.RData file 162341539458 100 MB
7 RIL_TMM_CPM.csv file 162352229544 630 MB
8 RIL_v1.5_mapping.tsv file 162218089685 110 MB
9 test.Rdata file 162921069376 64 B
owner
1 jnmaloof@ucdavis.edu
2 jnmaloof@ucdavis.edu
3 jnmaloof@ucdavis.edu
4 jnmaloof@ucdavis.edu
5 jnmaloof@ucdavis.edu
6 jnmaloof@ucdavis.edu
7 jnmaloof@ucdavis.edu
8 jnmaloof@ucdavis.edu
9 jnmaloof@ucdavis.edu
Use as.data.frame() to extract full results.
First attempt, use RIL means
Get data
annotation <- read_csv("~/Box Sync/BrapaNetworks/Brapa_V1.5_annotated.csv.gz")
Error: could not find function "read_csv"
Summarize means
save(voom.fit,fil="~/Box Sync/BrapaNetworks/voom.fit.Rdata")
Error in save(voom.fit, fil = "~/Box Sync/BrapaNetworks/voom.fit.Rdata") :
object ‘~/Box Sync/BrapaNetworks/voom.fit.Rdata’ not found
RIL.coefs <- grep("RIL",colnames(coef(voom.fit)))
RIL.pvals <- topTable(voom.fit,coef = RIL.coefs,number = Inf)
sum(RIL.pvals$adj.P.Val<0.05)
[1] 26721
sum(RIL.pvals$adj.P.Val<10e-10)
[1] 16596
So pretty much everything is highly significant
Start by taking the top 10000
genes.of.interest <- row.names(RIL.pvals)[1:10000]
expr.data <- voom.fit$coefficients[genes.of.interest,]
expr.data <- expr.data[,-grep("trt",colnames(expr.data))]
dim(expr.data)
[1] 10000 124
head(expr.data[,1:6])
(Intercept) pdata$RILRIL_103 pdata$RILRIL_104 pdata$RILRIL_113
Bra001964 1.082235 -4.0321896 -4.091759090 3.6994071
Bra002728 5.767475 -0.5505380 -0.380674984 -3.2039164
Bra002785 7.202490 -0.4198373 -0.037837754 -1.9963410
Bra004960 4.733936 -3.8185006 -0.004181862 -0.2564264
Bra006384 4.969959 -6.8228958 -7.276735466 -0.3784078
Bra006411 -2.499584 6.3062481 -0.470837158 0.1143828
pdata$RILRIL_115 pdata$RILRIL_12
Bra001964 0.01722016 0.64266465
Bra002728 -0.60526951 -0.81853556
Bra002785 -0.04677711 -0.05993573
Bra004960 0.14101788 -0.22825526
Bra006384 -8.39926386 -7.41766487
Bra006411 -0.72175621 -1.10891611
expr.data[,-1] <- expr.data[,-1] + expr.data[,1] # add the intercept to the coefficients
colnames(expr.data) <- sub("pdata$RIL","",colnames(expr.data),fixed=TRUE)
head(expr.data[,1:6])
(Intercept) RIL_103 RIL_104 RIL_113 RIL_115 RIL_12
Bra001964 1.082235 -2.9499545 -3.009524 4.781642 1.099455 1.724900
Bra002728 5.767475 5.2169367 5.386800 2.563558 5.162205 4.948939
Bra002785 7.202490 6.7826522 7.164652 5.206149 7.155712 7.142554
Bra004960 4.733936 0.9154359 4.729755 4.477510 4.874954 4.505681
Bra006384 4.969959 -1.8529365 -2.306776 4.591552 -3.429305 -2.447706
Bra006411 -2.499584 3.8066642 -2.970421 -2.385201 -3.221340 -3.608500
Get Rob’s Data
blups2011 <- box_read_csv(164446516413)
Remote file '/var/folders/xr/9cbydt955pj42zfq6mc_y5g40000gn/T//Rtmp7o2lKa/Brapa2011BayesHeight_blups.csv' read into memory as an object of class data.frame
blups2012 <- box_read_csv(164446639332)
Remote file '/var/folders/xr/9cbydt955pj42zfq6mc_y5g40000gn/T//Rtmp7o2lKa/Brapa2012BayesHeight_blups.csv' read into memory as an object of class data.frame
blups2011$Line %<>% paste("RIL",.,sep="_")
blups2012$Line %<>% paste("RIL",.,sep="_")
blups2011 %<>% subset(select=!grepl("V1|individual|blk|trt|treat",colnames(.)))
blups2012 %<>% subset(select=!grepl("V1|individual|blk|trt|treat",colnames(.)))
head(blups2011)
head(blups2012)
Merge Data
expr.blup.2011 <- merge(blups2011,t(expr.data),by.x="Line",by.y=0)
expr.blup.2012 <- merge(blups2012,t(expr.data),by.x="Line",by.y=0)
head(expr.blup.2011)
head(expr.blup.2012)
Correlation
expr.blup.2011.cor <- cor(expr.blup.2011[,-1])
expr.blup.2012.cor <- cor(expr.blup.2012[,-1])
Mutual Rank
sfStop()
Stopping cluster
save(blups2011,blups2012,voom.fit,expr.blup.2011, expr.blup.2012,expr.blup.2011.mr, expr.blup.2012.mr,file = "~/Box Sync/BrapaNetworks/MR.Rdata")
Get graphs at different MR
First: define some functions
Get blup networks at different MR thresholds
Use different MR thresholds of 10 to 210 (absolute values) to build the network (genes with a correlation higher than the threshold were considered connected). I then subset the network to take all genes “directly” connected to LFY.
plots
null device
1
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1
---
title: "MR Clustering"
output: html_notebook
---

Notebook for generating mutual rank (MR) clusters

```{r}
library(boxr)
library(magrittr)
library(knitr)
library(limma)
library(snowfall)
library(igraph)
box_auth()
box_setwd(24503987860)
box_ls()
```

### First attempt, use RIL means
Get data
```{r}
#box_load(162362592785)
load("~/Box Sync/BrapaNetworks/CountsVoom.Rdata")
annotation  <- read.csv("~/Box Sync/BrapaNetworks/Brapa_V1.5_annotated.csv.gz")
```

Summarize means
```{r}
str(counts.voom)
summary(counts.voom)
voom.fit <- lmFit(counts.voom,design=counts.voom$design) #additive design is already there
voom.fit <- eBayes(voom.fit)
save(voom.fit,file="~/Box Sync/BrapaNetworks/voom.fit.Rdata")
```


```{r}
RIL.coefs <- grep("RIL",colnames(coef(voom.fit)))
RIL.pvals <- topTable(voom.fit,coef = RIL.coefs,number = Inf)
sum(RIL.pvals$adj.P.Val<0.05)
sum(RIL.pvals$adj.P.Val<10e-10) 
```
So pretty much everything is highly significant

Start by taking the top 10000

```{r}
genes.of.interest <- row.names(RIL.pvals)[1:10000]
expr.data <- voom.fit$coefficients[genes.of.interest,]
expr.data <- expr.data[,-grep("trt",colnames(expr.data))]
dim(expr.data)
head(expr.data[,1:6])
```

```{r}
expr.data[,-1] <- expr.data[,-1] + expr.data[,1] # add the intercept to the coefficients
colnames(expr.data) <- sub("pdata$RIL","",colnames(expr.data),fixed=TRUE)
head(expr.data[,1:6])
```

Get Rob's Data
```{r}
blups2011 <- box_read_csv(164446516413)
blups2012 <- box_read_csv(164446639332)
blups2011$Line %<>% paste("RIL",.,sep="_")
blups2012$Line %<>% paste("RIL",.,sep="_")
blups2011 %<>% subset(select=!grepl("V1|individual|blk|trt|treat",colnames(.)))
blups2012 %<>% subset(select=!grepl("V1|individual|blk|trt|treat",colnames(.)))
head(blups2011)
head(blups2012)
```

Merge Data
```{r}
expr.blup.2011 <- merge(blups2011,t(expr.data),by.x="Line",by.y=0)
expr.blup.2012 <- merge(blups2012,t(expr.data),by.x="Line",by.y=0)
head(expr.blup.2011)
head(expr.blup.2012)
```

Correlation
```{r}
expr.blup.2011.cor <- cor(expr.blup.2011[,-1])
expr.blup.2012.cor <- cor(expr.blup.2012[,-1])
```

Mutual Rank
```{r}
sfInit(parallel = TRUE, cpus=4)

expr.blup.2011.rank <- sfApply(expr.blup.2011.cor,2,function(x) rank(-abs(x)))
expr.blup.2011.rank[1:10,1:10]
expr.blup.2011.mr <- sqrt(expr.blup.2011.rank*t(expr.blup.2011.rank))
expr.blup.2011.mr[1:10,1:10]

expr.blup.2012.rank <- sfApply(expr.blup.2012.cor,2,function(x) rank(-abs(x)))
expr.blup.2012.rank[1:10,1:10]
expr.blup.2012.mr <- sqrt(expr.blup.2012.rank*t(expr.blup.2012.rank))
expr.blup.2012.mr[1:10,1:10]

sfStop()
```


```{r}
save(blups2011,blups2012,voom.fit,expr.blup.2011, expr.blup.2012,expr.blup.2011.mr, expr.blup.2012.mr,file = "~/Box Sync/BrapaNetworks/MR.Rdata")
```

Get graphs at different MR

First: define some functions
 
```{r mr_subgraphs, echo=FALSE, cache.lazy=FALSE, warning=FALSE, eval=TRUE }
load("~/Box Sync/BrapaNetworks/MR.Rdata")
get.mr.subgraph <- function(mr.cutoff,mr.matrix,annotation=NA,neighborhood,order=1) {
  #function to extract LFY graph at a specificed correlation cutoff
  gene.mr.tmp <- mr.matrix
  gene.mr.tmp[abs(gene.mr.tmp) > mr.cutoff] <- 0
  gene.mr.tmp[is.na(gene.mr.tmp)] <- 0 #important! otherwise vertices with NA edges are connected
  
  gene.graph <- graph.adjacency(adjmatrix = gene.mr.tmp,
                                mode="undirected",
                                weighted="mr",
                                diag=FALSE)
  
  #clust.membership <- clusters(gene.graph)$membership
  
  #colbar <- rainbow(max(clust.membership)+1)                 #define colors
  #V(gene.graph)$color <- colbar[clust.membership+1]             #assign colors to nodes
  sub.graphs <- graph.neighborhood(gene.graph,order=order,nodes=neighborhood) #for each ndoe of interest get all other nodes within order of 1
  
  #get combnined list of vertices...
  
  sub.vertices <- unique(names(unlist(sapply(sub.graphs, V))))
  
  combined.sub.graph <- induced_subgraph(gene.graph,sub.vertices)
  
  V(combined.sub.graph)$color <- "lightblue"
  V(combined.sub.graph)[neighborhood]$color <- "red"
  if(!is.na(annotation)) V(combined.sub.graph)$gene <- annotation$SYMBOL[match(V(combined.sub.graph)$name,annotation$TAIR)]
  list(cutoff=mr.cutoff,graph=combined.sub.graph)
  }
```

## Get blup networks at different MR thresholds
Use different MR thresholds of 10 to 210 (absolute values) to build the network (genes with a correlation higher than the threshold were considered connected).  I then subset the network to take all genes "directly" connected to LFY.

```{r lfy_sub, echo=FALSE, eval=FALSE}
cutoffs <- c(10,20,30,50,80,100,130,210)
blup.mr.graphs.2011 <- lapply(cutoffs, get.mr.subgraph, mr.matrix = expr.blup.2011.mr, annotation= NA, neighborhood = colnames(blups2011)[-1])
names(blup.mr.graphs.2011) <- sapply(blup.mr.graphs.2011, function(x) paste("blup.mr.graph.2011",x$cutoff,sep="."))

blup.mr.graphs.2012 <- lapply(cutoffs, get.mr.subgraph, mr.matrix = expr.blup.2012.mr, annotation= NA, neighborhood = colnames(blups2012)[-1])
names(blup.mr.graphs.2012) <- sapply(blup.mr.graphs.2012, function(x) paste("blup.mr.graph.2012",x$cutoff,sep="."))
```

## plots

```{r plot, echo=FALSE, cache=TRUE}
cutoff=200
pdf("blup.mr.plots.2011.pdf",height=12,width=12)
result <- sapply(blup.mr.graphs.2011,function (blup.graph) {
  if(blup.graph$cutoff <= cutoff) {
    E(blup.graph$graph)$weight <- rank(E(blup.graph$graph)$mr)^(1/4)
    plot(blup.graph$graph,
         layout = layout_with_kk, 
         vertex.label = #ifelse(is.na(V(blup.graph$graph)$gene)|V(blup.graph$graph)$gene=="",
                               V(blup.graph$graph)$name ,
                            #   V(blup.graph$graph)$gene),
         vertex.label.cex=1,
         main=paste("MR cutoff =",sub("blup.mr.graph.","",blup.graph$cutoff,fixed=T)))
    rm(blup.graph)
    }
  })
dev.off()

cutoff=200
pdf("blup.mr.plots.2012.pdf",height=12,width=12)
result <- sapply(blup.mr.graphs.2012,function (blup.graph) {
  if(blup.graph$cutoff <= cutoff) {
    E(blup.graph$graph)$weight <- rank(E(blup.graph$graph)$mr)^(1/4)
    plot(blup.graph$graph,
         layout = layout_with_kk, 
         vertex.label = #ifelse(is.na(V(blup.graph$graph)$gene)|V(blup.graph$graph)$gene=="",
                               V(blup.graph$graph)$name ,
                            #   V(blup.graph$graph)$gene),
         vertex.label.cex=1,
         main=paste("MR cutoff =",sub("blup.mr.graph.","",blup.graph$cutoff,fixed=T)))
    rm(blup.graph)
    }
  })
dev.off()
```